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import numpy as np
import torch
import ModelInterfaces
class NeuralASR(ModelInterfaces.IASRModel):
word_locations_in_samples = None
audio_transcript = None
def __init__(self, model: torch.nn.Module, decoder) -> None:
"""
Initialize the NeuralASR (Audio Speech Recognition) model.
Args:
model (torch.nn.Module): The neural network model for ASR.
decoder: The decoder to convert CTC outputs to transcripts.
"""
super().__init__()
self.model = model
self.decoder = decoder # Decoder from CTC-outputs to transcripts
def getTranscript(self) -> str:
"""
Get the transcript of the processed audio.
Returns:
str: The audio transcript.
Raises:
AssertionError: If the audio has not been processed.
"""
assert self.audio_transcript is not None, 'Can get audio transcripts without having processed the audio'
return self.audio_transcript
def getWordLocations(self) -> list:
"""
Get the word locations from the processed audio.
Returns:
list: A list of word locations in samples.
Raises:
AssertionError: If the audio has not been processed.
"""
assert self.word_locations_in_samples is not None, 'Can get word locations without having processed the audio'
return self.word_locations_in_samples
def processAudio(self, audio: torch.Tensor) -> None:
"""
Process the audio to generate transcripts and word locations.
Args:
audio (torch.Tensor): The input audio tensor.
"""
audio_length_in_samples = audio.shape[1]
with torch.inference_mode():
nn_output = self.model(audio)
self.audio_transcript, self.word_locations_in_samples = self.decoder(
nn_output[0, :, :].detach(), audio_length_in_samples, word_align=True)
class NeuralTTS(ModelInterfaces.ITextToSpeechModel):
def __init__(self, model: torch.nn.Module, sampling_rate: int) -> None:
"""
Initialize the NeuralTTS (Text to Speech) model.
Args:
model (torch.nn.Module): The neural network model for TTS.
sampling_rate (int): The sampling rate for the audio.
"""
super().__init__()
self.model = model
self.sampling_rate = sampling_rate
def getAudioFromSentence(self, sentence: str) -> np.array:
"""
Generate audio from a given sentence.
Args:
sentence (str): The input sentence.
Returns:
np.array: The generated audio as a numpy array.
"""
with torch.inference_mode():
audio_transcript = self.model.apply_tts(texts=[sentence],
sample_rate=self.sampling_rate)[0]
return audio_transcript
class NeuralTranslator(ModelInterfaces.ITranslationModel):
def __init__(self, model: torch.nn.Module, tokenizer) -> None:
"""
Initialize the NeuralTranslator model.
Args:
model (torch.nn.Module): The neural network model for translation.
tokenizer: The tokenizer for text processing.
"""
super().__init__()
self.model = model
self.tokenizer = tokenizer
def translateSentence(self, sentence: str) -> str:
"""
Translate a given sentence to the target language.
Args:
sentence (str): The input sentence.
Returns:
str: The translated sentence.
"""
tokenized_text = self.tokenizer(sentence, return_tensors='pt')
translation = self.model.generate(**tokenized_text)
translated_text = self.tokenizer.batch_decode(
translation, skip_special_tokens=True)[0]
return translated_text
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